Garments with finishing patterns created by laser and neural network

ABSTRACT

Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. A technique includes using machine learning to create or extract a laser input file for wear pattern from an existing garment. Machine learning can be by a generative adversarial network, having generative and discriminative neural nets. The generative adversarial network is trained and then used to create a model. This model is used generate the laser input file from an image of the existing garment with the finishing pattern. With this laser input file, a laser can re-create the wear pattern from the existing garment onto a new garment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims the benefit of U.S. patent application62/579,867, filed Oct. 31, 2017, which is incorporated by referencealong with all other references cited in this application.

BACKGROUND OF THE INVENTION

The present invention relates to apparel finishing and, morespecifically, the use of a laser in the finishing of garments,especially denim including jeans, shirts, shorts, jackets, vests, andskirts, to obtain a faded, distressed, washed, or worn finish orappearance.

In 1853, during the California Gold Rush, Levi Strauss, a 24-year-oldGerman immigrant, left New York for San Francisco with a small supply ofdry goods with the intention of opening a branch of his brother's NewYork dry goods business. Shortly after arriving in San Francisco, Mr.Strauss realized that the miners and prospectors (called the “fortyniners”) needed pants strong enough to last through the hard workconditions they endured. So, Mr. Strauss developed the now familiarjeans which he sold to the miners. The company he founded, Levi Strauss& Co., still sells jeans and is the most widely known jeans brand in theworld. Levi's is a trademark of Levi Strauss & Co. or LS&Co.

Though jeans at the time of the Gold Rush were used as work clothes,jeans have evolved to be fashionably worn everyday by men and women,showing up on billboards, television commercials, and fashion runways.Fashion is one of the largest consumer industries in the U.S. and aroundthe world. Jeans and related apparel are a significant segment of theindustry.

As fashion, people are concerned with the appearance of their jeans.Many people desire a faded or worn blue jeans look. In the past, jeansbecame faded or distressed through normal wash and wear. The apparelindustry recognized people's desire for the worn blue jeans look andbegan producing jeans and apparel with a variety of wear patterns. Thewear patterns have become part of the jeans style and fashion. Someexamples of wear patterns include combs or honeycombs, whiskers, stacks,and train tracks.

Despite the widespread success jeans have enjoyed, the process toproduce modern jeans with wear patterns takes processing time, hasrelatively high processing cost, and is resource intensive. A typicalprocess to produce jeans uses significant amounts of water, chemicals(e.g., bleaching or oxidizing agents), ozone, enzymes, and pumice stone.For example, it may take about 20 to 60 liters of water to finish eachpair of jeans.

Therefore, there is a need for an improved process for finishing jeansthat reduces environmental impact, processing time, and processingcosts, while maintaining the look and style of traditional finishingtechniques. Further there is a need for improved techniques of creatingor extracting design features or wear patterns from existing garments toobtain corresponding laser input files.

BRIEF SUMMARY OF THE INVENTION

Software and lasers are used in finishing apparel to produce a desiredwear pattern or other design. A technique includes using machinelearning to create or extract a laser input file for wear pattern froman existing garment. A machine learning method may use a generativeadversarial network, having both generative and discriminative neuralnets that work together to translate an image of a garment tomanufacturing inputs that will recreate it. The generative adversarialnetwork model would then be trained with images of garments and themanufacturing inputs used to create them. This model is used to generatethe laser input file from an image of the existing garment with thefinishing pattern. With this laser input file, a laser can re-create thewear pattern from the existing garment onto a new garment. Anothertechnique using a purely convolutional neural network architectureallows for the creation of novel garments by mixing the aesthetic.

Wear patterns and other designs on garments (including jeans and otherdenim garments) are reproduced by capturing digital images (e.g., highresolution digital photographs, potentially in a raw format) of existinggarments exhibiting desirable wear patterns or other designs, processingthe digital images using machine learning software (e.g., generativeadversarial network), and then using the images generated by a modelformed from the machine learning software as the patterns to control alaser to reproduce the desired pattern or design on a new garment. Thisprocess permits the reproduction of desirable, complex, and authenticwear patterns taken from worn garments such as jeans on new articles ofclothing before sale.

In an implementation, a method includes: assembling a garment made fromfabric panels of a woven first material having a warp with indigoring-dyed cotton yarn, where the fabric panels are sewn together usingthread; using a model formed through a machine learning having agenerative adversarial network, creating a laser input file that isrepresentative of a finishing pattern from an existing garment made froma second material, where the first material has a different fabriccharacteristic from the second material; and using a laser to create afinishing pattern on an outer surface of the garment based on the laserinput file created by the model, where based on the laser input file,the laser removes selected amounts of material from the surface of thefirst material at different pixel locations of the garment, for lighterpixel locations of the finishing pattern, a greater amount of the indigoring-dyed cotton warp yarn is removed, while for darker pixel locationsof the finishing pattern, a lesser amount of the indigo ring-dyed cottonwarp yarn is removed, and the finishing pattern created can extendacross portions of the garment where two or more fabric panels arejoined together by the threads by exposing these portions to the laser.

In an implementation, a method includes: providing an assembled garmentmade from fabric panels of a woven first material including a warphaving indigo ring-dyed cotton yarn, where the fabric panels are sewntogether using thread; providing a laser input file that isrepresentative of a finishing pattern from an existing garment made froma second material, where the finishing pattern on the existing garmentwas not created by a laser, and the laser input file was obtained bytraining a generative adversarial network having a generative neural netand a discriminative neural net, and forming a model from the generativeadversarial network, where the model generates the laser input file foran image of the existing garment with the finishing pattern; and using alaser to create a finishing pattern on an outer surface of the assembledgarment based on the laser input file, where based on the laser inputfile, the laser removes selected amounts of material from the surface ofthe first material at different pixel locations of the assembledgarment, for lighter pixel locations of the finishing pattern, a greateramount of the indigo ring-dyed cotton warp yarn is removed, while fordarker pixel locations of the finishing pattern, a lesser amount of theindigo ring-dyed cotton warp yarn is removed, and the finishing patterncreated can extend across portions of the assembled garment where two ormore fabric panels are joined together by the threads by exposing theseportions to the laser.

In an implementation, a method includes: assembling a jean made fromfabric panels of a woven first denim material including a warp havingindigo ring-dyed cotton yarn, where the fabric panels are sewn togetherusing thread; creating a laser input file that is representative of afinishing pattern from an existing jean made from a second denimmaterial, where the first denim material includes a different fabriccharacteristic from the second denim material, and the creating thelaser input file includes using machine learning to form a model, wherethe model generates the laser input file for an image of the existinggarment with the finishing pattern; and using a laser to create afinishing pattern on an outer surface of the jean based on the laserinput file, where based on the laser input file, the laser removesselected amounts of material from the surface of the first material atdifferent pixel locations of the jean, for lighter pixel locations ofthe finishing pattern, a greater amount of the indigo ring-dyed cottonwarp yarn is removed, while for darker pixel locations of the finishingpattern, a lesser amount of the indigo ring-dyed cotton warp yarn isremoved, and the finishing pattern created can extend across portions ofthe jean where two or more fabric panels are joined together by thethreads by exposing these portions to the laser.

In an implementation, a method includes: providing an assembled garmentmade from fabric panels of a woven first material with a warp havingindigo ring-dyed cotton yarn, where the fabric panels are sewn togetherusing thread; and providing a laser input file that is representative ofa finishing pattern from an existing garment made from a secondmaterial, where the finishing pattern on the existing garment was notcreated by a laser. The laser input file was obtained by: providingsample laser input files and images of sample garments with laseredfinishing patterns resulting from the sample laser input files to agenerative adversarial network, where the sample laser input files arereal laser input files for the generative adversarial network; using agenerative neural net of the generative adversarial network, generatingfake laser input files for the images of sample garments with laseredfinishing patterns; determining a generator loss based on the fake laserinput files and real laser input files; inputting the real laser inputfiles to a real discriminator of the generative adversarial network;inputting the real laser input files and the fake laser input files to afake discriminator of the generative adversarial network; determining adiscriminator loss based on outputs of the real discriminator and fakediscriminator; and based on outputs of the generator loss anddiscriminator loss, iteratively training a model to obtain final model,where the final model generates the laser input file for an image of theexisting garment with the finishing pattern.

A laser is used to create a finishing pattern on an outer surface of theassembled garment based on the laser input file form the final model.Based on the laser input file, the laser removes selected amounts ofmaterial from the surface of the first material at different pixellocations of the assembled garment. For lighter pixel locations of thefinishing pattern, a greater amount of the indigo ring-dyed cotton warpyarn is removed, while for darker pixel locations of the finishingpattern, a lesser amount of the indigo ring-dyed cotton warp yarn isremoved. The finishing pattern created can extend across portions of theassembled garment where two or more fabric panels are joined together bythe threads by exposing these portions to the laser.

In an implementation, a garment includes: fabric panels made from awoven first material with a warp having dyed cotton yarn, where thefabric panels are sewn together using thread; and an outer surface ofthe garment includes a finishing pattern created by a laser based on alaser input file. The laser input file includes digital data that isrepresentative of a finishing pattern from an existing garment made froma second material. The first material has a different fabriccharacteristic from the second material. The laser input file is createdby providing sample laser input files and images of sample garments withlasered finishing patterns resulting from the sample laser input filesto a generative adversarial network, where the sample laser input filesare real laser input files for the generative adversarial network; usinga generative neural net of the generative adversarial network,generating fake laser input files for the images of sample garments withlasered finishing patterns; determining a generator loss based on thefake laser input files and real laser input files; inputting the reallaser input files to a real discriminator of the generative adversarialnetwork; inputting the real laser input files and the fake laser inputfiles to a fake discriminator of the generative adversarial network;determining a discriminator loss based on outputs of the realdiscriminator and fake discriminator; and based on outputs of thegenerator loss and discriminator loss, iteratively training a model toobtain final model, where the final model generates the laser input filefor an image of the existing garment with the finishing pattern.

In an implementation, a garment includes: fabric panels made from awoven first material with a warp having dyed cotton yarn, where thefabric panels are sewn together using thread; and an outer surface ofthe garment includes a finishing pattern created by a laser based on alaser input file. The laser input file includes digital data that isrepresentative of a finishing pattern from an existing garment made froma second material. The first material has a different fabriccharacteristic from the second material. The laser input file is createdby training a generative adversarial network comprising a generativeneural net and a discriminative neural net, and forming a model from thegenerative adversarial network, where the model generates the laserinput file for an image of the existing garment with the finishingpattern.

Other objects, features, and advantages of the present invention willbecome apparent upon consideration of the following detailed descriptionand the accompanying drawings, in which like reference designationsrepresent like features throughout the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of a system for creating, designing,producing apparel products with laser finishing.

FIG. 2 shows a flow for a finishing technique that includes the use of alaser.

FIG. 3 shows a weave pattern for a denim fabric.

FIGS. 4-7 show how the laser alters the color of ring-dyed yarn. FIG. 4shows a laser beam striking a ring-dyed yarn having indigo-dyed fibersand white core fibers. FIG. 5 shows the laser using a first power levelsetting or first exposure time setting, or a combination of these, toremove some of the dyed fibers, but not revealing any of the white corefibers. FIG. 6 shows the laser using a second power level setting orsecond exposure time setting, or a combination of these, to remove moreof the dyed fibers than in FIG. 5. FIG. 7 shows the laser using a thirdpower level setting or third exposure time setting, or a combination ofthese, to remove even more of the dyed fibers than in FIG. 6.

FIG. 8 shows a flow for finishing in two finishing steps and using basetemplates.

FIG. 9 shows multiple base templates and multiple resulting finishedproducts from each of these templates.

FIG. 10 shows a distributed computer network.

FIG. 11 shows a computer system that can be used in laser finishing.

FIG. 12 shows a system block diagram of the computer system.

FIG. 13 shows a block diagram of machine learning using a generativeadversarial network to generate laser input files for realistic wearpatterns on apparel.

FIG. 14 shows another block diagram of machine learning using agenerative adversarial network to generate laser input files forrealistic wear patterns on apparel.

FIG. 15 shows a technique of a convolutional style transfer by way of astyle transfer convolutional network.

FIG. 16 shows another example of a convolutional style transfer.

FIG. 17 has shows a more detailed system diagram of a conditionalgenerative adversarial neural network (cGAN) to generate laser inputfiles for realistic wear patterns on apparel.

FIG. 18 shows an individual operation module for a conditionalgenerative adversarial neural network to generate laser input files forrealistic wear patterns on apparel.

FIGS. 19A-19C show an implementation of a generator of a generativeadversarial neural network to generate laser input files for realisticwear patterns on apparel. FIG. 19A shows a first portion of a generatorof a generative adversarial neural network. FIG. 19B shows a secondportion of the generator of the generative adversarial neural network.FIG. 19C shows a third portion of the generator of the generativeadversarial neural network.

FIGS. 20A-20C show another implementation of a generator of a generativeadversarial neural network to generate laser input files for realisticwear patterns on apparel. FIG. 20A shows a first portion of a generatorof a generative adversarial neural network. FIG. 20B shows a secondportion of the generator of the generative adversarial neural network.FIG. 20C shows a third portion of the generator of the generativeadversarial neural network.

FIG. 21 shows an implementation of a discriminator of a generativeadversarial neural network to generate laser input files for realisticwear patterns on apparel.

FIG. 22 shows an overall block diagram of a loss structure of thegenerative adversarial neural network to generate laser input files forrealistic wear patterns on apparel.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows a process flow 101 for manufacturing apparel such as jeans,where garments are finished using a laser. The fabric or material forvarious apparel including jeans is made from natural or synthetic fibers106, or a combination of these. A fabric mill takes fibers and processes109 these fibers to produce a laser-sensitive finished fabric 112, whichhas enhanced response characteristics for laser finishing.

Some examples of natural fibers include cotton, flax, hemp, sisal, jute,kenaf, and coconut; fibers from animal sources include silk, wool,cashmere, and mohair. Some examples of synthetic fibers includepolyester, nylon, spandex or elastane, and other polymers. Some examplesof semisynthetic fibers include rayon, viscose, modal, and lyocell,which are made from a regenerated cellulose fiber. A fabric can be anatural fiber alone (e.g., cotton), a synthetic fiber alone (e.g.,polyester alone), a blend of natural and synthetic fibers (e.g., cottonand polyester blend, or cotton and spandax), or a blend of natural andsemisynthetic fibers, or any combination of these or other fibers.

For jeans, the fabric is typically a denim, which is a sturdy cottonwarp-faced textile in which a weft passes under two or more warpthreads. This twill weaving produces a diagonal ribbing. The yarns(e.g., warp yarns) are dyed using an indigo or blue dye, which ischaracteristic of blue jeans.

Although this patent describes the apparel processing and finishing withrespect to jeans, the invention is not limited jeans or denim products,such as shirts, shorts, jackets, vests, and skirts. The techniques andapproaches described are applicable to other apparel and products,including nondenim products and products made from knit materials. Someexamples include T-shirts, sweaters, coats, sweatshirts (e.g., hoodies),casual wear, athletic wear, outerwear, dresses, evening wear, sleepwear,loungewear, underwear, socks, bags, backpacks, uniforms, umbrellas,swimwear, bed sheets, scarves, and many others.

A manufacturer creates a design 115 (design I) of its product. Thedesign can be for a particular type of clothing or garment (e.g., men'sor women's jean, or jacket), sizing of the garment (e.g., small, medium,or large, or waist size and inseam length), or other design feature. Thedesign can be specified by a pattern or cut used to form pieces of thepattern. A fabric is selected and patterned and cut 118 based on thedesign. The pattern pieces are assembled together 121 into the garment,typically by sewing, but can be joined together using other techniques(e.g., rivets, buttons, zipper, hoop and loop, adhesives, or othertechniques and structures to join fabrics and materials together).

Some garments can be complete after assembly and ready for sale.However, other garments are unfinished 122 and have additional finishing124, which includes laser finishing. The finishing may include tinting,washing, softening, and fixing. For distressed denim products, thefinishing can include using a laser to produce a wear pattern accordingto a design 127 (design II). Some additional details of laser finishingare described in U.S. patent applications 62/377,447, filed Aug. 19,2016, and Ser. No. 15/682,507, filed Aug. 21, 2017, issued as U.S. Pat.No. 10,051,905 on Aug. 21, 2018, are incorporated by reference alongwith all other references cited in this application.

Design 127 is for postassembly aspects of a garment while design 115 isfor preassembly aspects of a garment. After finishing, a finishedproduct 130 (e.g., a pair of jeans) is complete and ready for sale. Thefinished product is inventoried and distributed 133, delivered to stores136, and sold to consumers or customers 139. The consumer can buy andwear worn blue jeans without having to wear out the jeans themselves,which usually takes significant time and effort.

Traditionally, to produce distressed denim products, finishingtechniques include dry abrasion, wet processing, oxidation, or othertechniques, or combinations of these, to accelerate wear of the materialin order to produce a desired wear pattern. Dry abrasion can includesandblasting or using sandpaper. For example, some portions or localizedareas of the fabric are sanded to abrade the fabric surface. Wetprocessing can include washing in water, washing with oxidizers (e.g.,bleach, peroxide, ozone, or potassium permanganate), spraying withoxidizers, washing with abrasives (e.g., pumice, stone, or grit).

These traditional finishing approaches take time, incur expense, andimpact the environment by utilizing resources and producing waste. It isdesirable to reduce water and chemical usage, which can includeeliminating the use agents such as potassium permanganate and pumice. Analternative to these traditional finishing approaches is laserfinishing.

FIG. 2 shows a finishing technique that includes the use of a laser 207.A laser is a device that emits light through a process of opticalamplification based on the stimulated emission of electromagneticradiation. Lasers are used for bar code scanning, medical proceduressuch as corrective eye surgery, and industrial applications such aswelding. A particular type of laser for finishing apparel is a carbondioxide laser, which emits a beam of infrared radiation.

The laser is controlled by an input file 210 and control software 213 toemit a laser beam onto fabric at a particular position or location at aspecific power level for a specific amount of time. Further, the powerof the laser beam can be varied according to a waveform such as a pulsewave with a particular frequency, period, pulse width, or othercharacteristic. Some aspects of the laser that can be controlled includethe duty cycle, frequency, marking or burning speed, and otherparameters.

The duty cycle is a percentage of laser emission time. Some examples ofduty cycle percentages include 40, 45, 50, 55, 60, 80, and 100 percent.The frequency is the laser pulse frequency. A low frequency might be,for example, 5 kilohertz, while a high frequency might be, for example,25 kilohertz. Generally, lower frequencies will have higher surfacepenetration than high frequencies, which has less surface penetration.

The laser acts like a printer and “prints,” “marks,” or “burns” a wearpattern (specified by input file 210) onto the garment. The fabric thatis exposed to the laser beam (e.g., infrared beam) changes color,lightening the fabric at a specified position by a certain amount basedon the laser power, time of exposure, and waveform used. The lasercontinues from position to position until the wear pattern is completelyprinted on the garment.

In a specific implementation, the laser has a resolution of about 34dots per inch (dpi), which on the garment is about 0.7 millimeters perpixel. The technique described in this patent is not dependent on thelaser's resolution, and will work with lasers have more or lessresolution than 34 dots per inch. For example, the laser can have aresolution of 10, 15, 20, 25, 30, 40, 50, 60, 72, 80, 96, 100, 120, 150,200, 300, or 600 dots per inch, or more or less than any of these orother values. Typically, the greater the resolution, the finer thefeatures that can be printed on the garment in a single pass. By usingmultiple passes (e.g., 2, 3, 4, 5, or more passes) with the laser, theeffective resolution can be increased. In an implementation, multiplelaser passes are used.

Jeans are dyed using an indigo dye, which results in a blue coloredfabric. The blue color is caused by chromophores trapped in the fabricwhich reflect light as a blue color. U.S. patent application 62/433,739,filed Dec. 13, 2016, which is incorporated by reference, describes adenim material with enhanced response characteristics to laserfinishing. Using a denim material made from indigo ring-dyed yarn,variations in highs and lows in indigo color shading is achieved byusing a laser.

FIG. 3 shows a weave pattern of a denim fabric 326. A loom does theweaving. In weaving, warp is the lengthwise or longitudinal yarn orthread in a roll, while weft or woof is the transverse thread. The weftyarn is drawn through the warp yarns to create the fabric. In FIG. 3,the warps extend in a first direction 335 (e.g., north and south) whilethe wefts extend in a direction 337 (e.g., east and west). The wefts areshown as a continuous yarn that zigzags across the wefts (e.g., carriedacross by a shuttle or a rapier of the loom). Alternatively, the weftscould be separate yarns. In some specific implementations, the warp yarnhas a different weight or thickness than the weft yarns. For example,warp yarns can be coarser than the weft yarns.

For denim, dyed yarn is used for the warp, and undyed or white yarn istypically used for the weft yarn. In some denim fabrics, the weft yarncan be dyed and have a color other than white, such as red. In the denimweave, the weft passes under two or more warp threads. FIG. 3 shows aweave with the weft passing under two warp threads. Specifically, thefabric weave is known as a 2×1 right-hand twill. For a right-hand twill,a direction of the diagonal is from a lower left to an upper right. Fora left-hand twill, a direction of the diagonal is from an lower right toan upper left. But in other denim weaves, the weft can pass under adifferent number of warp threads, such as 3, 4, 5, 6, 7, 8, or more. Inother implementation, the denim is a 3×1 right-hand twill, which meansthe weft passes under three warp threads.

Because of the weave, one side of the fabric exposes more of the warpyarns (e.g., warp-faced side), while the other side exposes more of theweft yarns (e.g., weft-faced side). When the warp yarns are blue andweft yarns are white, a result of the weave is the warp-faced side willappear mostly blue while the reverse side, weft-faced side, will appearmostly white.

In denim, the warp is typically 100 percent cotton. But some warp yarnscan be a blend with, for example, elastane to allow for warp stretch.And some yarns for other fabrics may contain other fibers, such aspolyester or elastane as examples.

In an indigo ring-dyed yarn, the indigo does not fully penetrate to acore of the yarn. Rather, the indigo dye is applied at a surface of thecotton yarn and diffuses toward the interior of the yarn. So when theyarn is viewed cross-sectionally, the indigo dyed material will appearas a ring on around an outer edge of the yarn. The shading of the indigodye will generally lighten in a gradient as a distance increases fromthe surface of the yarn to the center (or core) of the yarn.

During laser finishing, the laser removes a selected amount of thesurface of the indigo dyed yarn (e.g., blue color) to reveal a lightercolor (e.g., white color) of the inner core of the ring-dyed yarn. Themore of the indigo dyed material that is removed, the lighter the color(e.g., lighter shade of blue). The more of the indigo dyed material thatremains, the darker the color (e.g., deeper shade of blue). The lasercan be controlled precisely to remove a desired amount of material toachieve a desired shade of blue in a desired place or position on thematerial.

With laser finishing, a finish can be applied (e.g., printed or burnedvia the laser) onto apparel (e.g., jeans and denim garments) that willappear similar to or indistinguishable from a finish obtained usingtraditional processing techniques (e.g., dry abrasion, wet processing,and oxidation). Laser finishing of apparel is less costly and is fasterthan traditional finishing techniques and also has reduced environmentalimpact (e.g., eliminating the use of harsh chemical agents and reducingwaste).

FIGS. 4-7 show how the laser alters the color of ring-dyed yarn. FIG. 4shows a laser beam 407 striking a ring-dyed yarn 413 having indigo-dyedfibers 418 and white core fibers 422. The laser removes the dyed fibers,which can be by vaporizing or otherwise destroying the cotton fiber viaheat or high temperature that the laser beam causes. Before lasering, across section of the warp of the target garment comprises a generallyround shape.

FIG. 5 shows the laser using a first power level setting or firstexposure time setting, or a combination of these, to remove some of thedyed fibers, but not revealing any of the white core fibers. The undyedfibers remain covered. There is no color change. After being exposed tothe laser and the depth of material that has been removed, a crosssection of the warp comprises a region with a flattened shape relativeto the generally round shape before lasering.

FIG. 6 shows the laser using a second power level setting or secondexposure time setting, or a combination of these, to remove more of thedyed fibers than in FIG. 5. The second power level is greater than thefirst power level, or the second exposure time setting is greater thanthe first exposure time setting, or a combination of these. The resultis some of the undyed fibers are revealed. There is a color change,subtle highlighting. As for FIG. 5, after being exposed to the laser andthe depth of material that has been removed, a cross section of the warpcomprises a region with a flattened shape relative to the generallyround shape before lasering.

FIG. 7 shows the laser using a third power level setting or thirdexposure time setting, or a combination of these, to remove even more ofthe dyed fibers than in FIG. 6. The third power level is greater thanthe second power level, or the third exposure time setting is greaterthan the second exposure time setting, or a combination of these. Theresult is more of the undyed fibers are revealed. There is a colorchange, brighter highlighting. As for FIGS. 5 and 6, after being exposedto the laser and the depth of material that has been removed, a crosssection of the warp comprises a region with a flattened shape relativeto the generally round shape before lasering.

As shown in FIG. 2, before laser 207, the fabric can be prepared 216 forthe laser, which may be referred to as a base preparation, and caninclude a prelaser wash. This step helps improves the results of thelaser. After the laser, there can be a postlaser wash 219. This wash canclean or remove any residue caused by the laser, such as removing anycharring (which would appear as brown or slightly burned). There can beadditional finish 221, which may be including tinting, softening, orfixing, to complete finishing.

FIG. 8 shows a technique where finishing 124 is divided into twofinishing steps, finishing I and finishing II. Finishing I 808 is aninitial finishing to create base templates 811. With finishing II 814,each base template can be used to manufacture multiple final finishes817.

FIG. 9 shows multiple base templates, base A, base B, and base C. Thesebase templates may be referred to as base fit fabrics or BFFs. In animplementation, the base templates can be created during base prep andprelaser wash 216 (see FIG. 2). During finishing I, by using differentwash 216 methods or recipes, each different base template can becreated.

Finishing II can include laser finishing. Base A is lasered withdifferent designs to obtain various final product based on base A (e.g.,FP(A)1 to FP(A)i, where i is an integer). Base B is lasered withdifferent designs to obtain various final product based on base B (e.g.,FP(B)1 to FP(B)j, where j is an integer). Base C is lasered withdifferent designs to obtain various final product based on base C (e.g.,FP(C)1 to FP(C)k, where k is an integer). Each base can be used toobtain a number of different final designs. For example, the integers i,j, and k can have different values.

As described above and shown in FIG. 2, after finishing II, there can beadditional finishing during post laser wash 219 and additional finishing221. For example, during the postlaser wash, there may be additionaltinting to the lasered garments. This tinting can result in an overallcolor cast to change the look of the garment.

In an implementation, laser finishing is used to create many differentfinishes (each a different product) easily and quickly from the samefabric template or BFF or “blank.” For each fabric, there will be anumber of base fit fabrics. These base fit fabrics are lasered toproduce many different finishes, each being a different product for aproduct line. Laser finishing allows greater efficiency because by usingfabric templates (or base fit fabrics), a single fabric or material canbe used to create many different products for a product line, more thanis possible with traditional processing. This reduces the inventory ofdifferent fabric and finish raw materials.

For a particular product (e.g., 511 product), there can be two differentfabrics, such as base B and base C of FIG. 9. The fabrics can be part ofa fabric tool kit. For base B, there are multiple base fit fabrics,FP(B)1, FP(B)2, and so forth. Using laser finishing, a base fit fabric(e.g., FP(B)1) can be used to product any number of different finishes(e.g., eight different finishes), each of which would be considered adifferent product model.

For example, FP(B)1 can be laser finished using different laser files(e.g., laser file 1, laser file 2, laser file 3, or others) or havedifferent postlaser wash (e.g., postlaser wash recipe 1, postlaser washrecipe 2, postlaser wash recipe 3, or others), or any combination ofthese. A first product would be base fit fabric FP(B)1 lasered usinglaser file 1 and washed using postlaser wash recipe 1. A second productwould be base fit fabric FP(B)1 lasered using laser file 2 and washedusing postlaser wash recipe 1. A third product would be base fit fabricFP(B)1 lasered using laser file 2 and washed using postlaser wash recipe2. And there can be many more products based on the same base fitfabric. Each can have a different product identifier or uniqueidentifier, such as a different PC9 or nine-digit product code.

With laser finishing, many products or PC9s are produced for each basefit fabric or blank. Compared to traditional processing, this is asignificant improvement in providing greater numbers of differentproducts with less different fabrics and finishes (each of which intraditional processing consume resources, increasing cost, and taketime). Inventory is reduced. The technique of providing base fitfinishes or fabric templates for laser finishing has significant andmany benefits.

A system incorporating laser finishing can include a computer to controlor monitor operation, or both. FIG. 10 shows an example of a computerthat is component of a laser finishing system. The computer may be aseparate unit that is connected to a system, or may be embedded inelectronics of the system. In an embodiment, the invention includessoftware that executes on a computer workstation system or server, suchas shown in FIG. 10.

FIG. 10 is a simplified block diagram of a distributed computer network1000 incorporating an embodiment of the present invention. Computernetwork 1000 includes a number of client systems 1013, 1016, and 1019,and a server system 1022 coupled to a communication network 1024 via aplurality of communication links 1028. Communication network 1024provides a mechanism for allowing the various components of distributednetwork 1000 to communicate and exchange information with each other.

Communication network 1024 may itself be comprised of manyinterconnected computer systems and communication links. Communicationlinks 1028 may be hardwire links, optical links, satellite or otherwireless communications links, wave propagation links, or any othermechanisms for communication of information. Communication links 1028may be DSL, Cable, Ethernet or other hardwire links, passive or activeoptical links, 3G, 3.5G, 4G and other mobility, satellite or otherwireless communications links, wave propagation links, or any othermechanisms for communication of information.

Various communication protocols may be used to facilitate communicationbetween the various systems shown in FIG. 10. These communicationprotocols may include VLAN, MPLS, TCP/IP, Tunneling, HTTP protocols,wireless application protocol (WAP), vendor-specific protocols,customized protocols, and others. While in one embodiment, communicationnetwork 1024 is the Internet, in other embodiments, communicationnetwork 1024 may be any suitable communication network including a localarea network (LAN), a wide area network (WAN), a wireless network, anintranet, a private network, a public network, a switched network, andcombinations of these, and the like.

Distributed computer network 1000 in FIG. 10 is merely illustrative ofan embodiment incorporating the present invention and does not limit thescope of the invention as recited in the claims. One of ordinary skillin the art would recognize other variations, modifications, andalternatives. For example, more than one server system 1022 may beconnected to communication network 1024. As another example, a number ofclient systems 1013, 1016, and 1019 may be coupled to communicationnetwork 1024 via an access provider (not shown) or via some other serversystem.

Client systems 1013, 1016, and 1019 typically request information from aserver system which provides the information. For this reason, serversystems typically have more computing and storage capacity than clientsystems. However, a particular computer system may act as both as aclient or a server depending on whether the computer system isrequesting or providing information. Additionally, although aspects ofthe invention have been described using a client-server environment, itshould be apparent that the invention may also be embodied in astand-alone computer system.

Server 1022 is responsible for receiving information requests fromclient systems 1013, 1016, and 1019, performing processing required tosatisfy the requests, and for forwarding the results corresponding tothe requests back to the requesting client system. The processingrequired to satisfy the request may be performed by server system 1022or may alternatively be delegated to other servers connected tocommunication network 1024.

Client systems 1013, 1016, and 1019 enable users to access and queryinformation stored by server system 1022. In a specific embodiment, theclient systems can run as a standalone application such as a desktopapplication or mobile smartphone or tablet application. In anotherembodiment, a “Web browser” application executing on a client systemenables users to select, access, retrieve, or query information storedby server system 1022. Examples of Web browsers include the InternetExplorer browser program provided by Microsoft Corporation, Firefoxbrowser provided by Mozilla, Chrome browser provided by Google, Safaribrowser provided by Apple, and others.

In a client-server environment, some resources (e.g., files, music,video, or data) are stored at the client while others are stored ordelivered from elsewhere in the network, such as a server, andaccessible via the network (e.g., the Internet). Therefore, the user'sdata can be stored in the network or “cloud.” For example, the user canwork on documents on a client device that are stored remotely on thecloud (e.g., server). Data on the client device can be synchronized withthe cloud.

FIG. 11 shows an exemplary client or server system of the presentinvention. In an embodiment, a user interfaces with the system through acomputer workstation system, such as shown in FIG. 11. FIG. 11 shows acomputer system 1101 that includes a monitor 1103, screen 1105,enclosure 1107 (may also be referred to as a system unit, cabinet, orcase), speaker (not shown), keyboard or other human input device 1109,and mouse or other pointing device 1111. Mouse 1111 may have one or morebuttons such as mouse buttons 1113.

It should be understood that the present invention is not limited anycomputing device in a specific form factor (e.g., desktop computer formfactor), but can include all types of computing devices in various formfactors. A user can interface with any computing device, includingsmartphones, personal computers, laptops, electronic tablet devices,global positioning system (GPS) receivers, portable media players,personal digital assistants (PDAs), other network access devices, andother processing devices capable of receiving or transmitting data.

For example, in a specific implementation, the client device can be asmartphone or tablet device, such as the Apple iPhone (e.g., AppleiPhone 8, iPhone XS, or iPhone XS Max), Apple iPad (e.g., Apple iPad,Apple iPad Pro, or Apple iPad mini), Apple iPod (e.g, Apple iPod Touch),Samsung Galaxy product (e.g., Galaxy S series product or Galaxy Noteseries product), Google Nexus and Pixel devices (e.g., Google Nexus 6,Google Nexus 7, or Google Nexus 9), and Microsoft devices (e.g.,Microsoft Surface tablet). Typically, a smartphone includes a telephonyportion (and associated radios) and a computer portion, which areaccessible via a touch screen display.

There is nonvolatile memory to store data of the telephone portion(e.g., contacts and phone numbers) and the computer portion (e.g.,application programs including a browser, pictures, games, videos, andmusic). The smartphone typically includes a camera (e.g., front facingcamera or rear camera, or both) for taking pictures and video. Forexample, a smartphone or tablet can be used to take live video that canbe streamed to one or more other devices.

Enclosure 1107 houses familiar computer components, some of which arenot shown, such as a processor, memory, mass storage devices 1117, andthe like. Mass storage devices 1117 may include mass disk drives, floppydisks, magnetic disks, optical disks, magneto-optical disks, fixeddisks, hard disks, CD-ROMs, recordable CDs, DVDs, recordable DVDs (e.g.,DVD-R, DVD+R, DVD-RW, DVD+RW, HD-DVD, or Blu-ray Disc), flash and othernonvolatile solid-state storage (e.g., USB flash drive or solid statedrive (SSD)), battery-backed-up volatile memory, tape storage, reader,and other similar media, and combinations of these.

A computer-implemented or computer-executable version or computerprogram product of the invention may be embodied using, stored on, orassociated with computer-readable medium. A computer-readable medium mayinclude any medium that participates in providing instructions to one ormore processors for execution. Such a medium may take many formsincluding, but not limited to, nonvolatile, volatile, and transmissionmedia. Nonvolatile media includes, for example, flash memory, or opticalor magnetic disks. Volatile media includes static or dynamic memory,such as cache memory or RAM. Transmission media includes coaxial cables,copper wire, fiber optic lines, and wires arranged in a bus.Transmission media can also take the form of electromagnetic, radiofrequency, acoustic, or light waves, such as those generated duringradio wave and infrared data communications.

For example, a binary, machine-executable version, of the software ofthe present invention may be stored or reside in RAM or cache memory, oron mass storage device 1117. The source code of the software of thepresent invention may also be stored or reside on mass storage device1117 (e.g., hard disk, magnetic disk, tape, or CD-ROM). As a furtherexample, code of the invention may be transmitted via wires, radiowaves, or through a network such as the Internet.

FIG. 12 shows a system block diagram of computer system 1101 used toexecute the software of the present invention. As in FIG. 11, computersystem 1101 includes monitor 1103, keyboard 1109, and mass storagedevices 1117. Computer system 1101 further includes subsystems such ascentral processor (CPU) 1202, system memory 1204, input/output (I/O)controller 1206, display adapter 1208, serial or universal serial bus(USB) port 1212, network interface 1218, graphics processor (GPU) 1220,field programmable gate array (FPGA) 1225, and specialized processor 228(e.g., ASIC, physics processor, digital signal processor (DSP), or otherprocessor). The invention may also be used with computer systems withadditional or fewer subsystems. For example, a computer system couldinclude more than one processor 1202 (i.e., a multiprocessor system) ora system may include a cache memory.

The computer system may include any number of graphics processors. Thegraphics processor may reside on the motherboard such as beingintegrated with the motherboard chipset. One or more graphics processorsmay reside on external boards connected to the system through a bus suchas an ISA bus, PCI bus, AGP port, PCI Express, or other system buses.Graphics processors may on separate boards, each connected to a bus suchas the PCI Express bus and to each other and to the rest of the system.Further, there may be a separate bus or connection (e.g., Nvidia SLI orATI CrossFire connection) by which the graphics processors maycommunicate with each other. This separate bus or connection may be usedin addition to or in substitution for system bus.

The processor, CPU or GPU, or both, may be a dual core or multicoreprocessor, where there are multiple processor cores on a singleintegrated circuit. The system may also be part of a distributedcomputing environment. In a distributed computing environment,individual computing systems are connected to a network and areavailable to lend computing resources to another system in the networkas needed. The network may be an internal Ethernet network, Internet, orother network. Multiple processors (e.g., CPU, GPU, FPGA, and otherspecialized processors, in any combination) can be utilized on multiple,different machines connected by the network. These machines that performthe computation in parallel may connected through the Internet (orCloud) using a paradigm known as Cloud computing.

Arrows such as 1222 represent the system bus architecture of computersystem 1101. However, these arrows are illustrative of anyinterconnection scheme serving to link the subsystems. For example, GPU1220 could be connected to the other subsystems through a port or havean internal direct connection to central processor 1202. The processormay include multiple processors or a multicore processor, which maypermit parallel processing of information. Computer system 1101 shown inFIG. 12 is but an example of a computer system suitable for use with thepresent invention. Other configurations of subsystems suitable for usewith the present invention will be readily apparent to one of ordinaryskill in the art.

Computer software products may be written in any of various suitableprogramming languages, such as C, C++, C#, Pascal, Fortran, Perl, Matlab(from MathWorks, www.mathworks.com), SAS, SPSS, JavaScript, AJAX, Java,Python, Erlang, and Ruby on Rails. The computer software product may bean independent application with data input and data display modules.Alternatively, the computer software products may be classes that may beinstantiated as distributed objects. The computer software products mayalso be component software such as Java Beans (from Oracle Corporation)or Enterprise Java Beans (EJB from Oracle Corporation).

An operating system for the system may be one of the Microsoft Windows®family of systems (e.g., Windows 95, 98, Me, Windows NT, Windows 2000,Windows XP, Windows XP x64 Edition, Windows Vista, Windows 7, Windows 8,Windows 10, Windows CE, Windows Mobile, Windows RT), Symbian OS, Tizen,Linux, HP-UX, UNIX, Sun OS, Solaris, Mac OS X, Apple iOS, Android, AlphaOS, AIX, IRIX32, or IRIX64. Other operating systems may be used.Microsoft Windows is a trademark of Microsoft Corporation.

Any trademarks or service marks used in this patent are property oftheir respective owner. Any company, product, or service names in thispatent are for identification purposes only. Use of these names, logos,and brands does not imply endorsement.

Furthermore, the computer may be connected to a network and mayinterface to other computers using this network. The network may be anintranet, internet, or the Internet, among others. The network may be awired network (e.g., using copper), telephone network, packet network,an optical network (e.g., using optical fiber), or a wireless network,or any combination of these. For example, data and other information maybe passed between the computer and components (or steps) of a system ofthe invention using a wireless network using a protocol such as Wi-Fi(IEEE standards 802.11, 802.11a, 802.11b, 802.11e, 802.11g, 802.11i,802.11n, 802.11ac, and 802.11ad, just to name a few examples), nearfield communication (NFC), radio-frequency identification (RFID), mobileor cellular wireless (e.g., 2G, 3G, 4G, 3GPP LTE, WiMAX, LTE, LTEAdvanced, Flash-OFDM, HIPERMAN, iBurst, EDGE Evolution, UMTS, UMTS-TDD,ixRDD, and EV-DO). For example, signals from a computer may betransferred, at least in part, wirelessly to components or othercomputers.

In an embodiment, with a Web browser executing on a computer workstationsystem, a user accesses a system on the World Wide Web (WWW) through anetwork such as the Internet. The Web browser is used to download Webpages or other content in various formats including HTML, XML, text,PDF, and postscript, and may be used to upload information to otherparts of the system. The Web browser may use uniform resourceidentifiers (URLs) to identify resources on the Web and hypertexttransfer protocol (HTTP) in transferring files on the Web.

In other implementations, the user accesses the system through either orboth of native and nonnative applications. Native applications arelocally installed on the particular computing system and are specific tothe operating system or one or more hardware devices of that computingsystem, or a combination of these. These applications (which aresometimes also referred to as “apps”) can be updated (e.g.,periodically) via a direct internet upgrade patching mechanism orthrough an applications store (e.g., Apple iTunes and App store, GooglePlay store, Windows Phone store, and Blackberry App World store).

The system can run in platform-independent, nonnative applications. Forexample, client can access the system through a Web application from oneor more servers using a network connection with the server or serversand load the Web application in a Web browser. For example, a Webapplication can be downloaded from an application server over theInternet by a Web browser. Nonnative applications can also be obtainedfrom other sources, such as a disk.

Machine learning is used in apparel design to create realistic patternsand designs (e.g., wear pattern) on apparel. A realistic appearingpattern refers to a pattern that a person would not be able to (or wouldbe difficult to) discriminate as being a fake (e.g., generated ormanufactured artificially by a computer).

Machine learning is a field of computer science, and in particular,artificial intelligence (AI), that gives computers the ability toautomatically learn and improve from experience without being explicitlyprogrammed. Machine learning can utilize an artificial neural network(or simply, a neural net) that is based on a collection of connectedunits called artificial neurons.

Machine learning algorithms can be categorized as supervised orreinforcement learning type algorithms. Supervised machine learningalgorithms can apply what has been learned in the past to new data usinglabeled examples to predict future events. Starting from the analysis ofa known training dataset, the learning algorithm produces an inferredfunction to make predictions about the output values. The system is ableto provide targets for any new input after sufficient training. Thelearning algorithm can also compare its output with the correct,intended output and find errors in order to modify the modelaccordingly.

Unsupervised machine learning algorithms are used when the informationused to train is neither classified nor labeled. Unsupervised learningstudies how systems can infer a function to describe a hidden structurefrom unlabeled data. The system does not figure out the right output,but it explores the data and can draw inferences from datasets todescribe hidden structures from unlabeled data.

In an implementation, machine learning and artificial neural networkcomputations for apparel design are executed on a hardware or softwaresystem, or combination, comprising one or more specialized processingunits. Examples of the specialized processing units include centralprocessing units (CPUs), graphical processing units (GPUs), physicsprocessors, cell processors, digital signal processors (DSPs), fieldprogrammable gate arrays (FPGAs), application specific integratedcircuits (ASICs), and the like. Portions of the neural computationaltask for apparel design may be transformed into the form of mathematicalmanipulations. GPUs may be particularly well suited to performing suchoperations.

In this application, GPUs are used as an example of a specializedprocessor, but this is not intended to limit the scope of the teachingof this patent to GPUs. A neural net may utilize any of the specializedprocessors mentioned previously, and other substantially similarprocessors as understood by those having ordinary skills in the art andas similar or related processors may be developed later. An interfacefacilitating a neural net may be at least one of a PCI Express bus, AGPbus, front side bus, Ethernet, the Internet, or other interface thatfacilitates the transfer of data in any form including serial orparallel.

An alternative hardware configuration includes a cooperative collectionof specialized processing units where each processing unit may be wellsuited for a specific type of computation. This hardware configurationwill be defined here as a “heterogeneous configuration” meaning that thevarious computational tasks are executed by different, typicallyspecialized, processors. As an example, GPUs are designed specificallyfor high throughput on specialized types of problems found in graphicsprocessing that require a large number of arithmetic calculations with arelatively small number of memory access steps. Other specializedprocessors may be designed to handle other types of data orcomputational problems. Allocating the various portions of the neuralnet computations to specialized processors may improve the throughput,increase the efficiency, lower the cost, and improve the results of thecomputation.

GPUs may be designed for fast graphics processing. The data may beorganized into a stream where a stream is an ordered set of data of thesame data type. Operations, procedures, methods, algorithms, and thelike that may be applied to entire streams of data are typically calledkernels. Kernels are very efficient because they depend only on theirinput. Internal computations within the kernel are independent of otherelements of the stream. Therefore, GPUs may be designed for parallelprocessing, memory efficiency, and high throughput for specificproblems.

GPUs typically have hardware blocks that may be specifically designedfor certain types of problems (e.g., specific kernels may be implementedin hardware). As an example, hardware blocks may be designed toimplement various types of vector or matrix computations, or both. As anexample, graphics data is typically four-dimensional referring to thechannel value of the red, green, and blue pixels (referred to as RGB)and the opacity value (typically referred as alpha or A). Therefore,GPUs have been designed to process images (e.g., four-dimensional (RGBA)data) very quickly and very efficiently.

FIG. 13 shows a block diagram of machine learning using a generativeadversarial network (GAN) 1303 to generate laser input files forrealistic wear patterns on apparel. In an implementation, the generativeadversarial network executes on a system having multiple graphicsprocessors. The graphics processors or GPUs increase throughput, such asfor training of neural nets that are part of the generative adversarialnetwork and also for generating a model based on the training.

It should be understood that the invention is not limited to thespecific flows and steps presented. A flow of the invention may haveadditional steps (not necessarily described in this patent), differentsteps which replace some of the steps presented, fewer steps or a subsetof the steps presented, or steps in a different order than presented, orany combination of these. Further, the steps in other implementations ofthe invention may not be exactly the same as the steps presented and maybe modified or altered as appropriate for a particular application orbased on the data or situation.

A generative adversarial network is an architecture that typicallyincorporates some form of unsupervised machine learning. The generativeadversarial network includes at least two neural networks. A firstnetwork may be referred to as a generative (G) neural network 1304 andgenerates candidates 1307. A second network, which may be referred to asa discriminative (D) neural network 1309, evaluates the candidatesgenerated by the first network. The generative neural net tries tosynthesize fake images that fool the discriminative neural net intothinking that the images are actual laser input files, not fake.

In a specific implementation, the input to generative neural net areimages of garments with sample finished patterns with known laser inputfiles. The known laser input files may be referred to as real data.Taking the sample images of garments, generative neural net generateslaser input file candidates. These generated laser input file candidatescan be referred to as fake data.

The generative adversarial network is trained by inputting images oflaser input files with various patterns and designs 1312 and also imagesof the results of burning these laser input files onto apparel (e.g.,jeans) 1316. Over time, with more training from greater numbers of inputfiles, the generative neural net becomes better at generating laserinput file candidates that are capable of fooling the discriminativeneural net.

The amount of sample data used to train a generative adversarial networkcan vary, such as more than 100 samples, more than 200 samples, morethan 300 samples, more than 500 samples, more than 800 samples, morethan 900 samples, or more than 1000 samples. Generally, the greaternumber of samples, the better trained the generative adversarial networkwill. In an implementation, about 900 samples can be taken, which meansthere are 900 laser input files (real data) that are used to laser 900different finishing patterns on, for example, jeans. This amount of realdata sample can be further increased by various image processing orimage manipulation techniques (e.g., lightening or darkening theimages). The samples and image manipulated samples can be used as realdata for the generative adversarial network.

Some files are described as being of an image file type. Some examplesof image file types or file formats include bitmap or raster graphicsformats including IMG, TIFF, EXIF, JPEG, GIF, PNG, PBM, PGM, PPM, BMP,and RAW. The compression for the file can be lossless (e.g., TIFF) orlossy (e.g., JPEG). Other image file types or file formats includevector graphics including DXF, SVG, and the like.

Bitmaps or raster graphics are resolution dependent while vectorgraphics are resolution independent. Raster graphics generally cannotscale up to an arbitrary resolution without loss of apparent quality.This property contrasts with the capabilities of vector graphics, whichgenerally easily scale up to the quality of the device rendering them.

A raster graphics image is a dot matrix data structure representing agenerally rectangular grid of pixels, or points of color, viewable via amonitor, paper, or other display medium. A bitmap, such as a single-bitraster, corresponds bit-for-bit with an image displayed on a screen oroutput medium. A raster is characterized by the width and height of theimage in pixels and by the number of bits per pixel (or color depth,which determines the number of colors it can represent).

The BMP file format is an example of a bitmap. The BMP file format, alsoknown as bitmap image file or device independent bitmap (DIB) fileformat or simply a bitmap, is a raster graphics image file format usedto store bitmap digital images, independently of the display device. TheBMP file format is capable of storing two-dimensional digital images ofarbitrary width, height, and resolution, both monochrome and color, invarious color depths, and optionally with data compression, alphachannels, and color profiles.

The generative neural net generates laser input files and synthesizesimages to approximate a provided wear pattern (without a laser inputfile). The discriminative neural net evaluates each candidate generated.When the generated image appears photorealistic (or not fake) comparedto the given image (without laser input file), the generativeadversarial network has created or extracted a laser input file for adesign that previously did not exist.

Then, information from the generative neural net and discriminativeneural net are used to generate 1323 a model 1329. This model can beused to generate laser input files 1352 for given images of apparel withwear patterns 1357, where the results would appear realistic to thediscriminative neural net.

In a specific implementation, the model is given images of a vintagegarment with a wear pattern. Typically, there is not a laser input filefor the vintage garment, since the wear pattern on the vintage garmentwas created by long-term wear. A jeans manufacturer like Levi Strauss &Co. has produced many jeans with a variety of wear patterns. Themanufacturer has many existing wear pattern designs, which can includevintage wear patterns. Some wear patterns are referred to as authenticwear patterns which are the result of long-term wear. For example, acowboy or cowgirl may wear a pair of jeans while ranching tending tocattle, riding houses, and participating in rodeos, and so forth. Aminer may wear a pair of jeans while prospecting for gold, mining forcoal, excavating a cavern, riding a mine train, and so forth. The resultof the worker working in the jeans for a period of time (e.g., five ormore years) without washing them will be an authentic wear pattern.

The apparel manufacturer wants to reproduce these existing, vintage, orauthentic wear pattern designs (or portions or features of thesedesigns) on garments. A laser system can be used to reproduce the wearpattern on new garments in an accelerated fashion, so that it will nottake years to produce a garment.

An approach is to scan or take a photo of an existing garment with awear pattern. Then with this scan or photo, create an inverted grayscaleimage. With the converted image, a laser prints (or burns) the wearpattern on another garment. However, the result of this approach isgenerally a very poor reproduction of the original wear pattern. Theresulting wear pattern typically does not appear realistic, generallyappearing flat—where the highs and lows in the coloration appearcompressed.

There are reasons why this approach does not work. A reason is thematerial of the original garment and new garment are different. Thelaser has not been specifically configured for the characteristics ofthe material being burned. The scanning process or photo may not be aproper input file to control the laser for burning the patternaccurately.

Another approach for re-creating wear patterns is to enhance scans(e.g., via hand editing or hand drawing) of the existing pattern using aphoto editing tool such as Adobe Photoshop. The editing process may usea computer, keyboard, mouse, or pen tablet input device (e.g., Wacomtablet), or any combination of these. This process is generally timeconsuming because significant manual editing is involved.

Therefore, it is desirable to obtain or extract a laser input file forthis vintage garment, so that the vintage wear pattern may be reproducedmore easily by laser finishing. Thus, the model (created through thegenerative adversarial network) is used to generate a laser input fileoutput for this vintage garment. Then through laser finishing, a lasercan burn laser input file onto a jeans template to re-create the vintagegarment wear pattern. Using the approach in FIG. 13 and described above,the laser can re-create a wear pattern faster and more accurately thanany previous approaches

Furthermore, in order to operate and generate candidates, the generativeadversarial network has a randomness component which normally onlyensures that the final network is trained in a robust way (e.g., able togeneralize well to new inputs), and this randomness component can beincreased or decreased. Although the candidates generated by thegenerative network may not be photorealistic compared to the givenimage, these candidates may be suitable for use as new designs that hadnot be previously produced or manufactured. These candidates may be useda starting point for designs to make modifications to create newdesigns. Therefore, due to the randomness component (which can bevaried), the generative adversarial network can be used to generate manynew designs that have not previously existed. Another technique using apurely convolutional neural network architecture allows for the creationof novel garments by mixing the aesthetic qualities of a garment orother artwork with the structural content of another image.

FIG. 14 shows another block diagram of machine learning using agenerative adversarial network (GAN) 1462 to generate laser input filesfor realistic wear patterns on apparel. This system is similar to thenetwork shown in FIG. 13 and described above. There is a generativeneural net that generates laser input file candidates from images ofsample garments with lasered finishing patterns with known laser inputfiles. The known laser input files can be referred to as real data,while the generated laser input file can be referred to as fake data.

Compared to FIG. 13, in this network, discriminative neural net 1309 issplit into two neural nets, a real discriminator 1474 and fakediscriminator 1476. There are two loss components, a generator loss 1480(which is composed of a distance loss in Euclidean space and a GAN loss)and a discriminator loss 1483 (which is composed of a real discriminatorloss and a fake discriminator loss). Generator loss 1480 takes inputfrom generated laser input file candidates 1307 (fake data) and laserinput files data 1312 (real data) and outputs a distance loss value aswell as a generator loss from the fake discriminator. These are input toa train model n iterations 1486 component.

Discriminator loss 1483 takes input from real discriminator 1474 (whichreceives real data) and fake discriminator 1476 (which receives fakedata) and outputs a discriminator loss value, which is input to trainmodel n iterations 1486. An output of train model n iterations 1486 ismodel 1329. Train model n iterations 1486 iterates on many laser inputfiles and generated laser input file candidates until an error or lossbased on the generator loss and discriminator loss values is reduced orminimized. When the loss values are reduced to a minimum, desired, oracceptable level, then that model can be used to create the laser inputfiles from a given image of a jeans with finishing. An acceptable levelcan be determined by human judgment or review of a quality of thegenerated image.

Note that both the generator loss and discriminator loss take intoaccount fake data. This is an adversarial aspect of the network. Thegenerator is attempting to produce better fakes to fool thediscriminator, while the discriminator is trying to improve itself indetecting fakes. Through adversarial training, the system improves itsability to produce a model that is used generate laser input file for agiven image of a garment with the finishing pattern.

In short, to use this system, a user obtains a model by training thenetworks. This involves providing as input numerous examples of realdata. Specifically, the real data would include laser input files andphotos of the laser finished apparel which are from a result of laseringusing these laser input files.

The system is also trained on generated data or fake data. This wouldinclude neural-net-generated laser input files, from generative neuralnet 1304 and generate laser input file candidates 1307. Training of amodel continues until results from the generated laser input filesbecome indistinguishable from the real laser input files. That is, thegenerative adversarial network is unable to distinguish the differencewhether the image of apparel is a real or a fake.

At that point, when the model is given a photo of finished apparel forwhich a laser input file is not available, the model will be able togenerate an appropriate laser input file to obtain that finished apparelusing laser finishing.

An input to the real discriminator includes the input training or samplephotos or images. An input to the discriminator includes the generatedphotos from the generative neural network. A loss is then calculated bystrategically combine three loss measures, distance, discriminator, andgenerator. This can be done by the relationships (in Python programminglanguage and TensorFlow framework from Google) as follows:discrim_loss=tf.reduce_mean(−(tf.log(predictreal+EPS)+tf.log(1−predict_fake+EPS)))gen_loss GAN=reduce_mean(−tf.log(predict_fake+EPS))gen_loss_L1=tf.reduce_mean(tf.abs(targets−outputs))gen_loss=gen_loss_GAN*a.gan_weight+gen_loss L1*a.11_weight.

Note that both the generator and the discriminator loss both have“predict_fake” loss in the relationships. This means that thediscriminator and the generator both care about how good the “fake” orgenerated image is. To keep the discriminator loss “honest,” thetechnique factors in the ability to tell what a real image is and makesure that it can still correctly guess a real image taken from the datawhile calling a fake a fake. To keep the generator “honest,” thetechnique forces the generator to deal with the actual Euclideandistance from the intended output is. Because there is one discriminatorloss function that trains both the real and fake discriminator, both aredependent on each other; without the other each discriminator couldbecome biased. Similar relationship applies for the generator loss(e.g., combining predict_fake and distance loss).

FIG. 15 shows a technique of neural style transfer using a convolutionalneural network. This technique using a purely convolutional neuralnetwork architecture allows for the creation of novel garments by mixingthe aesthetic qualities from a laser pattern with the form factor (ortopology) from another separate garment. These combinations may be mixedand matched to create novel product.

In an upper row 1504, a style image (e.g., Starry Nights by Vincent vanGogh) is combined with an input image (e.g., photograph of homes along ariver), and the output from the convolutional neural network is an imagewith the style transferred (e.g., painting of the photograph in theStarry Nights style).

In a lower row 1512, a style image (e.g., laser input file) is combinedwith an input image (e.g., image of jeans), and the output from thestyle transfer convolutional network is an image with the styletransferred (e.g., jeans with wear pattern in the style of the styleimage).

FIG. 16 shows another example of a neural style transfer. In thisexample, the same input and style images are used to generate threedifferent outputs, which each differs from each other in appearance.This example shows how a style transfer convolutional network can alsobe used to generate multiple designs from the same input.

Artificial neural networks can be used for creative content development.A designer inputs style factors, and the technique generates novelfinish patterns. Artificial neural networks can stylize existingfinishes. Artificial neural networks can generate patterns for carryoverfinishes. Artificial neural networks can generate printable finishpatterns for nonspecific briefs (e.g., full package in a box).

FIG. 17 has shows a more detailed system diagram of a conditionalgenerative adversarial neural network (cGAN) to generate laser inputfiles for realistic wear patterns on apparel. The system can beimplemented in software using one or more general purpose computers,hardware with firmware code, or specialized hardware.

The components of the system include main 1705, load_examples( ) 1710,create_model 1715, create_discriminator 1720, create_discriminator 1725,create_genenerator 1730, batchnorm 1735, conv 1740, lrelu 1745, anddeconv 1750.

Main 1705 generates a paths output for load_examples 1710 and receivesexamples from load_examples( ). An initial setup occurs within main1705, taking in user variables and recording settings. Main checks apath or paths for input and output validity; creates a file list; makesinput data into tensor flow manipulatable objects; splits input datainto A side and B side, sets up data manipulation (e.g., flip or randomcrop), and calculates a model steps_per_epoch. Main returns RETURNExample{paths_batch, inputs_batch, targets_batch, len(input_paths),steps_per_epoch}.

Create_model 1715 receives examples.inputs and examples.target from mainand outputs a model to main. Create_model outputs inputs andout_channels to create_generators 1730, and receive output=layers[−1].In a specific implementation, create_model creates a first encoder layerwith a 4×4 convolution, stride 2 of the input; creates a list of encoderlayer definitions; create subsequent layers within a for loop iteratingover the list of layer definitions (e.g., lrelu→conv→batchnorm→appendlayer); and creates a list of decoder layer definitions including adropout percentage. Create_model creates decoder layers using a for loopto enumerate over the decoder layers. Skip connections (U-Net) areincluded where appropriate and appending to layers. A convolution (conv)operation is replaced by deconvolution (deconv) operation. A leakyrectified linear unit activation function (lrelu) function is replacedby a rectified linear unit activation function (relu) function. Seebelow for further discussion. Then, a final layer or generated image isreturned.

FIG. 18 shows an individual operation module for a conditionalgenerative adversarial neural network for laser finishing. The moduleblocks lists an operation 1808, planar square dimension 1816, and depthdimension 1824. Some examples of operations include: Input, Conv,DeConv, LReLu, ReLu, BatchNorm, and Dropout. Conv represents aconvolution operation. DeConv represents a transpose convolution ordeconvolution operation. ReLu represents a rectified linear unitactivation function. LReLu represents a leaky rectified linear unitactivation function.

FIGS. 19A-19C show an implementation of a generator architecture of agenerative adversarial neural network to generate laser input files forrealistic wear patterns on apparel. In this implementation, conv is a4×4 stride 2, deconv is a transpose convolution, and lrelu has a 0.2slope when a value is less than 0.

FIGS. 20A-20C show another implementation of a generator architecture ofa generative adversarial neural network to generate laser input filesfor realistic wear patterns on apparel.

Returning to FIG. 17, for a real discriminator operation, create_modelsoutputs inputs and targets to create_discriminator 1725, and receivespredict_real=layers[−1]. FIG. 20 shows an example of an implementationof operation of these components. For an implementation, conv are 4×4full depth filters, and lrelu has 0.2 slope when less than 0.

The operations include: concatenate disc_input and disc_target frominput, and targets are from the data set; create the first layer withinput→conv→lrelu; create subsequent layers within a for loop iterating aspecified number of discriminator layers (e.g.,conv→batchnorm→lrelu→append layer); create last layer with conv→sigmoid;and return final layer (e.g., 30×30 grade of “realness”).

Returning to FIG. 17, for a fake discriminator operation, create_modelsoutputs inputs and gen_output to create_discriminator 1720, and receivespredict_fake=layers[−1]. This is similar to the operation describedabove, but with predicted fake layers rather than predicted real layers.Similarly, FIG. 21 shows an implementation of a discriminatorarchitecture of a generative adversarial neural network to generatelaser input files for realistic wear patterns on apparel. For animplementation, conv are 4×4 full depth filters, and lrelu has 0.2 slopewhen less than 0.

Similarly, the operations include: concatenate disc_input anddisc_target from input, and targets are the generator outputs; createthe first layer with input→conv→lrelu; create subsequent layers within afor loop iterating a specified number of discriminator layers (e.g.,conv→batchnorm→lrelu→append layer); create last layer with conv→sigmoid;and return final layer (e.g., 30×30 grade of “fakeness”).

Returning to FIG. 17, the batchnorm, conv, lrelu, and deconv components(such as shown in FIGS. 19 and 20) represent the modules and theirinputs, which are generated by create_discriminator 1720,create_discriminator 1725, and create_generator.

FIG. 22 shows an overall block diagram of a loss structure of thegenerative adversarial neural network to generate laser input files forrealistic wear patterns on apparel. The components include inputs 2205,targets 2210, generator output image 2215, real discriminator 2220, fakediscriminator 2225, generator L1 loss 2230, generator GAN loss 2235,generator loss 2240, and discriminator loss 2245.

For the generative adversarial network, the real discriminator, fakediscriminator, and generator output image components are artificialneural networks of a machine learning system. The generator output imagecomponent generates candidates, while the real discriminator and fakediscriminator evaluate the candidates. In a specific implementation, thecandidates are laser image files used for laser finishing apparel suchas jeans.

Inputs 2205 include photos or images (e.g., photos of laser finishedgarments and laser input files used to produce the finished garments),which are provided as input to generator output image 2215, realdiscriminator 2220, and fake discriminator 2125 components. The realdiscriminator can take real images as input while the fake discriminatorcan take as input fake images. The generator output image receivesphotos (e.g., photos of laser finished garments with known laser inputfiles) and generates output images (e.g., laser image files or laserinput files) as candidates. The generated output images can be referredto as fake data, while the know laser input files can be referred to asreal data.

The real discriminator and fake discriminator components are differencemachines, comparing their inputs and determining whether a given imageis “real” or “fake.” Specifically, the real discriminator determinesdifferences between real images (from inputs 2205) and target images(from targets 2210). The fake discriminator determines differencesbetween fake images (from generator output image 2215) and inputs 2205.

Generator L1 loss 2230 determines a generator loss 2240 based on outputsof the generator output image and fake discriminator. Generator GAN loss2230 determines a discriminator loss 2245 based on outputs of the realand fake discriminators.

An equation (in Python programming language and TensorFlow frameworkfrom Google) for discrimator loss is:discrim_loss=tf.reduce_mean(−(tf.log(predict_real+EPS)+tf.log(1−predict_fake+EPS))).

Equations (in Python programming language and TensorFlow framework fromGoogle) for the generator loss are:gen_loss_GAN=tf.reduce_mean(−tf.log(predict_fake+EPS)),gen_loss_L1=tf.reduce_mean(tf.abs(targets−outputs)), andgen_loss=gen_loss_GAN*a.gan_weight+gen_loss_L1*a.11_weight.

More specifically, the fake discriminator uses the generator output aspart of its input and then outputs “predict_fake” which is used by thediscriminator loss to tell (part of) how good it is at detecting realversus fake. Output “predict_fake” is also used in generator loss todetermine how well it can fool the discriminator. In this way the lossfunction becomes data dependent and allows a benefit through adversarialtraining. The generator also uses a direct distance metric to determinehow close it is to what is in the data set. In this way the generatorfits to the solution in the dataset.

When using laser finishing to burn a pattern, various laser levels canbe obtained by varying an output of the laser beam by altering acharacteristic of a laser waveform such as a frequency, period, pulsewidth, power, duty cycle, or burning speed. The pattern can be formed bya single pass of the laser or multiple passes.

In an implementation, a system includes an assembled garment made of afabric material, where the assembled garment will be exposed to a laserbeam that will create a finishing pattern on a surface of the assembledgarment.

There is a laser that emits the laser beam, where the laser beam willform a finishing pattern on the surface of the fabric material of theassembled garment based on the laser input file. The laser input file isobtained by machine learning as discussed above. The laser input filecan be a reverse image.

The assembled garment can include fabric panels that have been sewntogether using thread to form pants legs, a crotch region for the pants,and pocket openings for the pants. Before exposure to the laser, theassembled garment does not have a finishing pattern. The fabric materialcan use a warp yarn having indigo ring-dyed cotton yarn and undyed weftyarn.

The finishing pattern on the surface of the fabric material of theassembled garment can be formed by removing a selected amount ofmaterial from the surface of the fabric material of the assembledgarment based on the laser input file. Laser levels at an output of thelaser beam are altered based on the laser input file by varying acharacteristic of a laser such as a frequency, period, pulse width,power, duty cycle, or burn speed.

In an implementation, a method includes assembling a jeans made fromfabric panels of a woven first denim material including a warp havingindigo ring-dyed cotton yarn, where the fabric panels are sewn togetherusing thread. A laser input file is created that is representative of afinishing pattern from an existing jeans made from a second denimmaterial. The first denim material has a different fabric characteristicfrom the second denim material. The creating the laser input file caninclude: using machine learning to form a model, where the modelgenerates the laser input file for an image of the existing garment withthe finishing pattern.

A laser is used to create a finishing pattern on an outer surface of thejeans based on a laser input file. Based on the laser input file, thelaser removes selected amounts of material from the surface of the firstmaterial at different pixel locations of the jeans. For lighter pixellocations of the finishing pattern, a greater amount of the indigoring-dyed cotton warp yarn is removed, while for darker pixel locationsof the finishing pattern, a lesser amount of the indigo ring-dyed cottonwarp yarn is removed. The finishing pattern created can extend acrossportions of the jeans where two or more fabric panels are joinedtogether by the threads by exposing these portions to the laser.

The first denim material can have a weft yarn that has not been indigodyed. For the portions of the jeans exposed to the laser where thefabric panels are joined, the fabric panels are joined together using athread having cotton.

The first denim material can have a first surface texture characteristicthat is different from a second surface texture characteristic of thesecond denim material. The first denim material can have a first dyecharacteristic that is different from a second dye characteristic of thesecond denim material. The first denim material can have a first basefabric color characteristic (e.g., color shade or color tint) that isdifferent from a second base fabric color characteristic of the seconddenim material. The first denim material can have a first yarncharacteristic (e.g., ring dye effect) that is different from a secondyarn characteristic of the second denim material. For example, thethickness of the ring dyed region can be different. The diameter of thecore region can be different.

Further, the first denim material can have a first yarn weightcharacteristic that is different from a second yarn weightcharacteristic of the second denim material. The first denim materialcan have a first yarn diameter characteristic that is different from asecond yarn diameter characteristic of the second denim material. Thefirst denim material can have a first yarn twist characteristic (e.g.,number of twists) that is different from a second yarn twistcharacteristic of the second denim material.

This description of the invention has been presented for the purposes ofillustration and description. It is not intended to be exhaustive or tolimit the invention to the precise form described, and manymodifications and variations are possible in light of the teachingabove. The embodiments were chosen and described in order to bestexplain the principles of the invention and its practical applications.This description will enable others skilled in the art to best utilizeand practice the invention in various embodiments and with variousmodifications as are suited to a particular use. The scope of theinvention is defined by the following claims.

The invention claimed is:
 1. A plurality of garments comprising: aplurality of sample garments with lasered finishing patterns resultingfrom a plurality of sample laser input files; a target garmentcomprising fabric panels made from a woven first material comprising awarp comprising dyed cotton yarn, wherein the fabric panels are sewntogether using thread; an outer surface of the target garment comprisesa finishing pattern created by a laser based on a laser input file,wherein before lasering, a cross section of the warp of the targetgarment comprises a generally round shape, after being exposed to thelaser and a depth of material that has been removed, a cross section ofthe warp comprises a region with a flattened shape relative to thegenerally round shape before lasering; and an existing garment made froma second material, wherein the first material comprises a differentfabric characteristic from the second material, and the existing garmentwas created before the target garment was created, the laser input filecomprises digital data that is representative of a finishing patternfrom the existing garment, sample laser input files and images of samplegarments with lasered finishing patterns that result from the samplelaser input files are input to a generative adversarial network, and thesample laser input files comprise real laser input files, fake laserinput files are generated by a generative neural net of the adversarialnetwork for images of sample garments with lasered finishing patterns, agenerator loss is determined based on the fake laser input files andreal laser input files, the real laser input files are input to a realdiscriminator and a fake discriminator of a generative adversarialnetwork, the fake laser input files are input to the fake discriminatorof the generative adversarial network, a discriminator loss isdetermined based on outputs of the real discriminator and fakediscriminator, a model is iteratively trained to obtain a final modelbased on outputs of the generator loss and discriminator loss, and thelaser input file for an image of the existing garment with the finishingpattern is generated by the final model.
 2. The garments of claim 1wherein the warp is ring dyed using an indigo dye, based on the laserinput file, selected amounts of material have been removed by the laserfrom the surface of the first material at different pixel locations ofthe target garment, and for lighter pixel locations of the finishingpattern, a greater amount of the dyed cotton warp yarn is removed,revealing a greater width of an inner core of the dyed yarn, while fordarker pixel locations of the finishing pattern, a lesser amount of thedyed cotton warp yarn is removed, revealing a lesser width of an innercore of the dyed yarn.
 3. The garments of claim 1 wherein the finishingpattern created can extend across portions of the target garment wheretwo or more fabric panels are joined together by thread by exposingthese portions to the laser.
 4. The garments of claim 1 wherein thefirst material comprises a weft comprising yarn that has not been dyed.5. The garments of claim 1 wherein for the portions of the targetgarment exposed to the laser where the fabric panels are joined, thefabric panels are joined together using a thread comprising cotton. 6.The garments of claim 1 wherein the finishing pattern on the outersurface of the target garment was created by a single pass of a laser.7. The garments of claim 1 wherein the finishing pattern on the outersurface of the target garment was created by multiple passes of a laser.8. The garments of claim 1 wherein a target image of the finishingpattern is captured from the existing garment of the second materialcomprises using contrast limited adaptive histogram equalization imageprocessing.
 9. The garments of claim 1 wherein to create the finishingpattern on the outer surface of the target garment, different laserlevels are obtained by varying an output of a laser beam by altering acharacteristic of the laser comprising at least one of a frequency,period, pulse width, power, duty cycle, or burning speed.
 10. Thegarments of claim 1 wherein the first material comprises a first surfacetexture characteristic which is different from a second surface texturecharacteristic of the second material.
 11. The garments of claim 1wherein the first material comprises a first dye characteristic which isdifferent from a second dye characteristic of the second material. 12.The garments of claim 1 wherein the first material comprises a firstbase fabric color characteristic which is different from a second basefabric color characteristic of the second material.
 13. The garments ofclaim 1 wherein the first material comprises a first yarn characteristicwhich is different from a second yarn characteristic of the secondmaterial.
 14. The garments of claim 1 wherein the first materialcomprises a first yarn weight characteristic which is different from asecond yarn weight characteristic of the second material.
 15. Thegarments of claim 1 wherein the first material comprises a first yarndiameter characteristic which is different from a second yarn diametercharacteristic of the second material.
 16. The garments of claim 1wherein the first material comprises a first yarn twist characteristicwhich is different from a second yarn twist characteristic of the secondmaterial.
 17. The garments of claim 1 wherein the finishing pattern onthe existing garment was not created by a laser.
 18. The garments ofclaim 1 wherein the finishing pattern created by the laser on the targetgarment includes a wear pattern comprising at least one of combs orhoneycombs, whiskers, stacks, or train tracks, or a combination.
 19. Aplurality of garments comprising: a target garment comprising fabricpanels made from a woven first material comprising a warp comprisingdyed cotton yarn, wherein the fabric panels are sewn together usingthread; an outer surface of the target garment comprises a finishingpattern created by a laser based on a laser input file, wherein beforelasering, a cross section of the warp of the target garment comprises agenerally round shape, after being exposed to the laser and a depth ofmaterial that has been removed, a cross section of the warp comprises aregion with a flattened shape relative to the generally round shapebefore lasering; and an existing garment made from a second material,wherein the first material comprises a different fabric characteristicfrom the second material, the existing garment with a finishing patternexisted before the target garment was created, and the finishing patternon the existing garment was not created by a laser, the laser input filecomprises digital data that is representative of the finishing patternfrom the existing garment, a generative adversarial network comprises agenerative neural net and a discriminative neural net, a model is formedfrom the generative adversarial network, and the laser input file for animage of the existing garment with the finishing pattern is generated bythe model.
 20. The garments of claim 19 wherein the garment comprises atleast one of jeans, shirts, shorts, jackets, vests, or skirts.
 21. Thegarments of claim 19 wherein the generative neural net generated fakelaser input files and the fake laser input files are input to thediscriminative neural net.